Robots That Can See: Leveraging Human Pose for Trajectory Prediction Article Swipe
Tim Salzmann
,
Hao-Tien Lewis Chiang
,
Markus Ryll
,
Dorsa Sadigh
,
Carolina Parada
,
Alex Bewley
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/lra.2023.3312035
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/lra.2023.3312035
Anticipating the motion of all humans in dynamic environments such as homes\nand offices is critical to enable safe and effective robot navigation. Such\nspaces remain challenging as humans do not follow strict rules of motion and\nthere are often multiple occluded entry points such as corners and doors that\ncreate opportunities for sudden encounters. In this work, we present a\nTransformer based architecture to predict human future trajectories in\nhuman-centric environments from input features including human positions, head\norientations, and 3D skeletal keypoints from onboard in-the-wild sensory\ninformation. The resulting model captures the inherent uncertainty for future\nhuman trajectory prediction and achieves state-of-the-art performance on common\nprediction benchmarks and a human tracking dataset captured from a mobile robot\nadapted for the prediction task. Furthermore, we identify new agents with\nlimited historical data as a major contributor to error and demonstrate the\ncomplementary nature of 3D skeletal poses in reducing prediction error in such\nchallenging scenarios.\n
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lra.2023.3312035
- OA Status
- green
- Cited By
- 19
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386453588
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386453588Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/lra.2023.3312035Digital Object Identifier
- Title
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Robots That Can See: Leveraging Human Pose for Trajectory PredictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-05Full publication date if available
- Authors
-
Tim Salzmann, Hao-Tien Lewis Chiang, Markus Ryll, Dorsa Sadigh, Carolina Parada, Alex BewleyList of authors in order
- Landing page
-
https://doi.org/10.1109/lra.2023.3312035Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.17209Direct OA link when available
- Concepts
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Computer science, Robot, Artificial intelligence, Trajectory, Computer vision, Task (project management), Mobile robot, Motion (physics), Doors, Machine learning, Human–computer interaction, Engineering, Astronomy, Operating system, Physics, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 10Per-year citation counts (last 5 years)
- References (count)
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58Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
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| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2309.17209 |
| primary_location.id | doi:10.1109/lra.2023.3312035 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S4210169774 |
| primary_location.source.issn | 2377-3766 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2377-3766 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IEEE Robotics and Automation Letters |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Robotics and Automation Letters |
| primary_location.landing_page_url | https://doi.org/10.1109/lra.2023.3312035 |
| publication_date | 2023-09-05 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W6846964981, https://openalex.org/W6639102338, https://openalex.org/W3175563878, https://openalex.org/W6839667351, https://openalex.org/W3112335585, https://openalex.org/W3035581100, https://openalex.org/W2963001155, https://openalex.org/W4292787291, https://openalex.org/W4312799843, https://openalex.org/W4360764676, https://openalex.org/W2985871763, https://openalex.org/W4383172002, https://openalex.org/W3139491754, https://openalex.org/W3034423770, https://openalex.org/W6755477022, https://openalex.org/W2962715980, https://openalex.org/W6739901393, https://openalex.org/W3167478287, https://openalex.org/W3108262631, https://openalex.org/W3131085900, https://openalex.org/W2895748257, https://openalex.org/W3096609285, https://openalex.org/W2971856312, https://openalex.org/W3046673529, https://openalex.org/W3131716792, https://openalex.org/W2963818059, https://openalex.org/W3144974485, https://openalex.org/W3109717189, https://openalex.org/W6801880476, https://openalex.org/W3035574168, https://openalex.org/W6842423778, https://openalex.org/W2101032778, https://openalex.org/W4312711111, https://openalex.org/W6780503301, https://openalex.org/W3203071852, https://openalex.org/W4383108355, https://openalex.org/W2962730651, https://openalex.org/W2962687116, https://openalex.org/W3108908812, https://openalex.org/W4214593147, https://openalex.org/W6779371077, https://openalex.org/W1970206276, https://openalex.org/W2532516272, https://openalex.org/W3156216502, https://openalex.org/W4385245566, https://openalex.org/W4283462010, https://openalex.org/W1861492603, https://openalex.org/W2928521819, https://openalex.org/W3037058446, https://openalex.org/W4313484219, https://openalex.org/W3116651890, https://openalex.org/W3108490973, https://openalex.org/W4285484024, https://openalex.org/W3202707544, https://openalex.org/W4311726826, https://openalex.org/W4296405214, https://openalex.org/W4294006621, https://openalex.org/W2953273646 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 100, 106, 122 |
| abstract_inverted_index.3D | 74, 132 |
| abstract_inverted_index.In | 51 |
| abstract_inverted_index.as | 10, 25, 42, 121 |
| abstract_inverted_index.do | 27 |
| abstract_inverted_index.in | 6, 135, 139 |
| abstract_inverted_index.is | 13 |
| abstract_inverted_index.of | 3, 32, 131 |
| abstract_inverted_index.on | 96 |
| abstract_inverted_index.to | 15, 59, 125 |
| abstract_inverted_index.we | 54, 114 |
| abstract_inverted_index.The | 81 |
| abstract_inverted_index.all | 4 |
| abstract_inverted_index.and | 18, 44, 73, 92, 99, 127 |
| abstract_inverted_index.are | 35 |
| abstract_inverted_index.for | 48, 88, 109 |
| abstract_inverted_index.new | 116 |
| abstract_inverted_index.not | 28 |
| abstract_inverted_index.the | 1, 85, 110 |
| abstract_inverted_index.data | 120 |
| abstract_inverted_index.from | 66, 77, 105 |
| abstract_inverted_index.safe | 17 |
| abstract_inverted_index.such | 9, 41 |
| abstract_inverted_index.this | 52 |
| abstract_inverted_index.based | 57 |
| abstract_inverted_index.doors | 45 |
| abstract_inverted_index.entry | 39 |
| abstract_inverted_index.error | 126, 138 |
| abstract_inverted_index.human | 61, 70, 101 |
| abstract_inverted_index.input | 67 |
| abstract_inverted_index.major | 123 |
| abstract_inverted_index.model | 83 |
| abstract_inverted_index.often | 36 |
| abstract_inverted_index.poses | 134 |
| abstract_inverted_index.robot | 20 |
| abstract_inverted_index.rules | 31 |
| abstract_inverted_index.task. | 112 |
| abstract_inverted_index.work, | 53 |
| abstract_inverted_index.agents | 117 |
| abstract_inverted_index.enable | 16 |
| abstract_inverted_index.follow | 29 |
| abstract_inverted_index.future | 62 |
| abstract_inverted_index.humans | 5, 26 |
| abstract_inverted_index.mobile | 107 |
| abstract_inverted_index.motion | 2, 33 |
| abstract_inverted_index.nature | 130 |
| abstract_inverted_index.points | 40 |
| abstract_inverted_index.remain | 23 |
| abstract_inverted_index.strict | 30 |
| abstract_inverted_index.sudden | 49 |
| abstract_inverted_index.corners | 43 |
| abstract_inverted_index.dataset | 103 |
| abstract_inverted_index.dynamic | 7 |
| abstract_inverted_index.offices | 12 |
| abstract_inverted_index.onboard | 78 |
| abstract_inverted_index.predict | 60 |
| abstract_inverted_index.present | 55 |
| abstract_inverted_index.achieves | 93 |
| abstract_inverted_index.captured | 104 |
| abstract_inverted_index.captures | 84 |
| abstract_inverted_index.critical | 14 |
| abstract_inverted_index.features | 68 |
| abstract_inverted_index.identify | 115 |
| abstract_inverted_index.inherent | 86 |
| abstract_inverted_index.multiple | 37 |
| abstract_inverted_index.occluded | 38 |
| abstract_inverted_index.reducing | 136 |
| abstract_inverted_index.skeletal | 75, 133 |
| abstract_inverted_index.tracking | 102 |
| abstract_inverted_index.effective | 19 |
| abstract_inverted_index.including | 69 |
| abstract_inverted_index.keypoints | 76 |
| abstract_inverted_index.resulting | 82 |
| abstract_inverted_index.and\nthere | 34 |
| abstract_inverted_index.benchmarks | 98 |
| abstract_inverted_index.historical | 119 |
| abstract_inverted_index.homes\nand | 11 |
| abstract_inverted_index.positions, | 71 |
| abstract_inverted_index.prediction | 91, 111, 137 |
| abstract_inverted_index.trajectory | 90 |
| abstract_inverted_index.challenging | 24 |
| abstract_inverted_index.contributor | 124 |
| abstract_inverted_index.demonstrate | 128 |
| abstract_inverted_index.encounters. | 50 |
| abstract_inverted_index.in-the-wild | 79 |
| abstract_inverted_index.navigation. | 21 |
| abstract_inverted_index.performance | 95 |
| abstract_inverted_index.uncertainty | 87 |
| abstract_inverted_index.Anticipating | 0 |
| abstract_inverted_index.Furthermore, | 113 |
| abstract_inverted_index.Such\nspaces | 22 |
| abstract_inverted_index.architecture | 58 |
| abstract_inverted_index.environments | 8, 65 |
| abstract_inverted_index.scenarios.\n | 141 |
| abstract_inverted_index.that\ncreate | 46 |
| abstract_inverted_index.trajectories | 63 |
| abstract_inverted_index.future\nhuman | 89 |
| abstract_inverted_index.opportunities | 47 |
| abstract_inverted_index.with\nlimited | 118 |
| abstract_inverted_index.a\nTransformer | 56 |
| abstract_inverted_index.robot\nadapted | 108 |
| abstract_inverted_index.state-of-the-art | 94 |
| abstract_inverted_index.in\nhuman-centric | 64 |
| abstract_inverted_index.such\nchallenging | 140 |
| abstract_inverted_index.common\nprediction | 97 |
| abstract_inverted_index.the\ncomplementary | 129 |
| abstract_inverted_index.head\norientations, | 72 |
| abstract_inverted_index.sensory\ninformation. | 80 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.89399001 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |